The three workflows where the audit pays for itself
Most of what we see falls into one of these. Each has clear money, risk, or compliance pressure — and a real architecture choice to make. The audit lands on closed API, open-weight, private cloud, hybrid, or no AI at all.
€2,000 · 2–3 weeks · 1–2 workflows · Written report
Customer support / ticket summarisation
What is the workflow? Inbound tickets are summarised, classified, and drafted-against by an AI assistant before an agent replies. Sometimes the AI auto-resolves; usually it suggests.
Why is it risky, costly, or manual? Customer PII and free-text complaints flow through a closed API on every ticket. Cost scales linearly with volume. Some teams cannot expose tickets to a US provider at all under their customer contracts.
What does Sobrn evaluate? Quality on your real ticket sample; per-ticket cost across closed API, open-weight, and private; what breaks if you switch model; how much of the workflow is actually classification (rule-able) versus generation (model-needed).
What decision do you get? Keep on a closed API; move summarisation private and keep drafting on API (hybrid); pre-classify deterministically to cut tokens by 30–60%; or stop — if the AI step is not earning its place.
Internal knowledge assistant using private documents
What is the workflow? Staff ask questions and an assistant answers from internal documents — policies, SOPs, product docs, contracts, HR materials.
Why is it risky, costly, or manual? Sensitive internal material has to be embedded and queried somewhere. Closed APIs raise data-residency and provider-trust questions; legal, HR, or works council may already be pushing back.
What does Sobrn evaluate? Whether retrieval quality is the bottleneck or generation quality is; cost of closed API at projected query volume versus a private deployment; what an open-weight model can and cannot answer well on your corpus; whether a smaller scope (one team, one doc set) makes more sense than a company-wide rollout.
What decision do you get? Move private (open-weight or private cloud) with a sized retrieval setup; keep on closed API but restrict the doc set; simplify (just publish the docs better); or no-go — users will not adopt it for reasons unrelated to AI.
Document review for legal, finance, compliance, or operations
What is the workflow? Long documents (contracts, supplier paper, financial filings, regulatory submissions, incident logs) are read, extracted, classified, or summarised — today by a mix of humans and AI.
Why is it risky, costly, or manual? Documents are confidential, often legally privileged. Per-document API cost rises fast with long context. Quality matters: misses are expensive. Vendors and customers ask where the data went.
What does Sobrn evaluate? Extraction quality on your real (or representative) documents; closed API vs open-weight on long-context tasks; cost per document at current and projected volume; what should stay deterministic (templates, regex, structured extraction) versus model-driven.
What decision do you get? Migrate the bulk to private (open-weight or private cloud) and keep long-context edge cases on closed API (hybrid); simplify with structured extraction where the document shape allows; or keep on closed API if volume does not justify the ops load.
Other workflow patterns we see
Less frequent than the three above, but the audit method is identical.
- B2B SaaS product AI. In-product copilots and generators mixing tenant data with product secrets.
- Sales / CRM cleanup. Note cleanup, meeting summaries, opportunity research briefs.
- HR / people processes. Policy Q&A, onboarding assistants, case-note triage.
- Operations triage. Supplier email triage, internal SOP assistants, incident log summarisation.
- Compliance & governance drafts. Control-testing narratives, policy gap checks, regulatory Q&A drafts for human review.
If you recognised your workflow above, book a fit call.
30 minutes. Founder-led. We will say quickly whether the audit is worth your money for this workflow.